Tying the knot between morphology and development: Using enamel-knot determined dental morphology to study the evolution of molarization in hoofed mammals
Bibliographic record
Abstract
Abstract Molariform teeth have fascinated zoologists for as long as the field of evolutionary biology has existed, but few mammalian groups show as much morphological variation as hoofed mammals. Ungulate premolars and molars function together as the post-canine unit in grinding mastication. The degree of similarity of the premolars to the molars in crown complexity varies wildly across dietary ecologies and similar morphologies are refered to as molarized. However, the vast majority of dental complexity evolution research over the past 30 years has focused on molar crown morphogenesis evolution rather than interregional dental phenomena such as molarization. Dental crown complexity in vertebrates is controlled by signalling centers known as enamel knots in all regions of the jaw. In this study we tested whether applying current knowledge of enamel knot driven crown morphogenesis to shape covariation across the premolar molar boundary would inform potential mechanisms of molarization in hoofed mammals. We used 2D geometric morphometrics to study enamel-knot driven covariation at the lower premolar molar boundaries of 16 artiodactyl and 18 perissodactyls species. Phylogenetically informed modularity analyses were used to test several a-priori morphogenetic hypotheses describing different developmental interactions between the premolars and molars. Our results showed artiodactyls and perissodactyls significantly differ in their premolar molar boundary covariation caused by heterochronic shifts between premolar and molar development. To our knowledge, our study is the first to contribute a comprehensive yet accesible 2D morphometric method to produce heuristic results for further investigating the evolution of molarized premolars.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".